Composite neural network load models for power system stability analysis

Ali Keyhani, Wenzhe Lu, Gerald T. Heydt

Research output: Chapter in Book/Report/Conference proceedingConference contribution

13 Scopus citations

Abstract

Proper load models are essential to power system stability analysis. This paper proposes a methodology for the development of neural network (NN) based composite load models for power system stability analysis. A two-step modeling procedure is proposed. First knowledge is acquired from a test bed of power systems based on detail load models of a bus to the distribution level. Then, the test bed data is used to develop a composite NN model. The developed NN model is updated based on measurements. A case study on a power inverter controling an induction motor load is presented.

Original languageEnglish (US)
Title of host publication2004 IEEE PES Power Systems Conference and Exposition
Pages1159-1163
Number of pages5
StatePublished - Dec 1 2004
Event2004 IEEE PES Power Systems Conference and Exposition - New York, NY, United States
Duration: Oct 10 2004Oct 13 2004

Publication series

Name2004 IEEE PES Power Systems Conference and Exposition
Volume2

Other

Other2004 IEEE PES Power Systems Conference and Exposition
CountryUnited States
CityNew York, NY
Period10/10/0410/13/04

    Fingerprint

Keywords

  • Artificial neural networks
  • Composite load modeling
  • Power systems
  • Stability analysis

ASJC Scopus subject areas

  • Engineering(all)

Cite this

Keyhani, A., Lu, W., & Heydt, G. T. (2004). Composite neural network load models for power system stability analysis. In 2004 IEEE PES Power Systems Conference and Exposition (pp. 1159-1163). (2004 IEEE PES Power Systems Conference and Exposition; Vol. 2).